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Title: Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures

Abstract

Selection of proper targets for the X-ray crystallography will benefit biological research community immensely. Several computational models were proposed to predict propensity of successful protein production and diffraction quality crystallization from protein sequences. We reviewed a comprehensive collection of 22 such predictors that were developed in the last decade. We found that almost all of these models are easily accessible as webservers and/or standalone software and we demonstrated that some of them are widely used by the research community. We empirically evaluated and compared the predictive performance of seven representative methods. The analysis suggests that these methods produce quite accurate propensities for the diffraction-quality crystallization. We also summarized results of the first study of the relation between these predictive propensities and the resolution of the crystallizable proteins. We found that the propensities predicted by several methods are significantly higher for proteins that have high resolution structures compared to those with the low resolution structures. Moreover, we tested a new meta-predictor, MetaXXC, which averages the propensities generated by the three most accurate predictors of the diffraction-quality crystallization. MetaXXC generates putative values of resolution that have modest levels of correlation with the experimental resolutions and it offers the lowest mean absolute errormore » when compared to the seven considered methods. Here, we conclude that protein sequences can be used to fairly accurately predict whether their corresponding protein structures can be solved using X-ray crystallography. Moreover, we also ascertain that sequences can be used to reasonably well predict the resolution of the resulting protein crystals.« less

Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
National Natural Science Foundation of China (NNSFC); National Institutes of Health (NIH); Research Fund for the Doctoral Program of Higher Education; International Development Research Centre (IDRC); Virginia Commonwealth University - Qimonda Endowed Chair; USDOE
OSTI Identifier:
1438826
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
Current Protein & Peptide Science
Additional Journal Information:
Journal Volume: 19; Journal Issue: 2; Journal ID: ISSN 1389-2037
Publisher:
Bentham Science Publishers
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; prediction; protein structure; X-ray crystallography; diffraction quality crystallization; meta prediction; protein production; resolution of protein crystals

Citation Formats

Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures. United States: N. p., 2017. Web. doi:10.2174/1389203718666170921114437.
Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures. United States. doi:10.2174/1389203718666170921114437.
Mon . "Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures". United States. doi:10.2174/1389203718666170921114437. https://www.osti.gov/servlets/purl/1438826.
@article{osti_1438826,
title = {Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures},
author = {None, None},
abstractNote = {Selection of proper targets for the X-ray crystallography will benefit biological research community immensely. Several computational models were proposed to predict propensity of successful protein production and diffraction quality crystallization from protein sequences. We reviewed a comprehensive collection of 22 such predictors that were developed in the last decade. We found that almost all of these models are easily accessible as webservers and/or standalone software and we demonstrated that some of them are widely used by the research community. We empirically evaluated and compared the predictive performance of seven representative methods. The analysis suggests that these methods produce quite accurate propensities for the diffraction-quality crystallization. We also summarized results of the first study of the relation between these predictive propensities and the resolution of the crystallizable proteins. We found that the propensities predicted by several methods are significantly higher for proteins that have high resolution structures compared to those with the low resolution structures. Moreover, we tested a new meta-predictor, MetaXXC, which averages the propensities generated by the three most accurate predictors of the diffraction-quality crystallization. MetaXXC generates putative values of resolution that have modest levels of correlation with the experimental resolutions and it offers the lowest mean absolute error when compared to the seven considered methods. Here, we conclude that protein sequences can be used to fairly accurately predict whether their corresponding protein structures can be solved using X-ray crystallography. Moreover, we also ascertain that sequences can be used to reasonably well predict the resolution of the resulting protein crystals.},
doi = {10.2174/1389203718666170921114437},
journal = {Current Protein & Peptide Science},
number = 2,
volume = 19,
place = {United States},
year = {2017},
month = {12}
}

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Figures / Tables:

Table 1 Table 1: Summary of protein sequence-based predictors of the propensity for protein cloning, material production, purification, crystallization and structure determination using X-ray crystallography (diffraction-quality crystallization). Bold font identifies methods that were used to perform empirical analysis.

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